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Forecasting Turkish Industrial Production Growth With Static Factor Models

Author

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  • Mahmut Günay

    (Yýldýrým Beyazýt University and The Central Bank of the Republic of Turkey.)

Abstract

In this paper, we forecast industrial production growth for the Turkish economy using static factor models. We evaluate how the performance of the models change based on the number of factors we extract from our data as well as the level of aggregation for the series in the data set. We consider two evaluation samples for the out-of-sample forecasting exercise to assess the stability of the forecasting performance. We find that the effect of the data set size on the forecasting performance is not independent from the number of factors extracted from this data set. Rankings of the models change in different evaluation samples. We conclude that using a dynamic approach to evaluate models from different dimensions is important in the forecasting process.

Suggested Citation

  • Mahmut Günay, 2015. "Forecasting Turkish Industrial Production Growth With Static Factor Models," International Econometric Review (IER), Econometric Research Association, vol. 7(2), pages 64-78, September.
    • Handle: RePEc:erh:journl:v:7:y:2015:i:2:p:64-78
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    References listed on IDEAS

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    More about this item

    Keywords

    Forecasting; Factor Models; Principal Components.;
    All these keywords.

    JEL classification:

    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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